Have someone introduce me. Thank audience (tie to morning activities), sponsors, HP, etc.
We’re here because this is the biggest thing that has happened to Hadoop…
Here at the conference we’re talking about data science. But before we can appreciate the changes happening in data science, we must first talk about Data. Data is doubling every two years. The fast growing volume, variety and velocity of data is overwhelming traditional systems and approaches. A revolutionary approach is required to leverage this data. And with this new technology, Data science as we know, is undergoing tremendous change.
To give you a sense of the data volumes that we’re talking about, I’ve included this chart that shows why a revolutionary approach is needed. You can see the amount of data growth moving from 1.8 Zettabytes to 44 Zettabytes in just over 5 years. To put this into perspective a large datawarehouse contains terabytes of data. A zettabye is 1 billion terabytes.
Numbers in chart are from two IDC reports (sponsored by emc). http://www.emc.com/collateral/about/news/idc-emc-digital-universe-2011-infographic.pdf http://www.emc.com/leadership/digital-universe/2014iview/executive-summary.htm
What is the source of this data growth? While structured data growth has been relatively modest, the growth in unstructured data has been exponential.
Source of statistic: http://link.springer.com/chapter/10.1007/978-3-642-39146-0_2
The database/datastore landscape is evolving to meet the new requirements. 2009 was the inflection point. NoSchema systems in which applications control structure. Developers are being empowered and they are voting for the agility offered by these systems.
In the early days if this revolution we sacrificed the query language, and we eliminated the ability to leverage the knowledge and tools available to millions of people. We’re changing that by a distributed SQL engine. But when we do that, we have to keep in mind that this transition to a NoSchema world happened for a reason, and we don’t want to reintroduce the centralized, DBA-managed schema.
TODO: Add Impala and Splunk logos
IT-driven = months of delay, unnecessary work (data is no longer relevant, etc.) The so-what needs to be conveyed. Why does it matter that it’s not needed.
6 months -> 3 months -> 3 months -> day zero
So imagine now what you can get…
Data Agility is needed for Business Agility
>>> Stand still during slide, move in at the punchline (why does this matter to YOU)
Organizations are realizing that they have to move towards self-service
All SQL engines (traditional or SQL-on-Hadoop) view tables as spreadsheet-like data structures with rows and columns. All records have the same structure, and there is no support for nested data or repeating fields. Drill views tables conceptually as collections of JSON (with additional types) documents. Each record can have a different structure (hence, schema-less). This is revolutionary and has never been done before.
If you consider the four data models shown in the 2x2, all models can be represented by the complex, no schema model (JSON) because it is the most flexible. However, no other data model can be represented by the flat, fixed schema model. Therefore, when using any SQL engine except Drill, the data has to be transformed before it can be available to queries.